Editing IPCC:AR6/WGIII/Chapter-8 (section)

== 8.3 Urban Systems and Greenhouse Gas Emissions ==

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This section assesses trends in urban land use, the built environment, and urban GHG emissions, as well as forecasts for urban land use and emissions under certain scenarios to 2050 or 2100. These trends and scenarios hold implications for optimising the approaches to urban climate change mitigation discussed in Sections 8.4 and 8.6.

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=== 8.3.1 Trends in Urban Land Use and the Built Environment ===

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Urban land use is one of the most intensive human impacts on the planet ( [[#Pouyat--2007|Pouyat et al. 2007]] ; [[#Grimm--2008|Grimm et al. 2008]] ). Urban land expansion to accommodate a growing urban population has resulted in the conversion of agricultural land ( [[#Pandey--2018|Pandey et al. 2018]] ; [[#Liu--2019|Liu et al. 2019]] ), deforestation ( [[#van%20Vliet--2019|van Vliet 2019]] ), habitat fragmentation ( [[#Liu--2016b|Liu et al. 2016b]] ), biodiversity loss ( [[#McDonald--2018|McDonald et al. 2018]] , 2020), and the modification of urban temperatures and regional precipitation patterns ( [[#Li--2017|Li et al. 2017]] ; [[#Krayenhoff--2018|Krayenhoff et al. 2018]] ; [[#Liu--2019|Liu and Niyogi 2019]] ; [[#Zhang--2019|Zhang et al. 2019]] ).

Urban land use and the associated built environment and infrastructure shape urban GHG emissions through the demand for materials and the ensuing energy-consuming behaviours. In particular, the structure of the built environment (i.e., its density, form, and extent) have long-lasting influence on urban GHG emissions, especially those from transport and building energy use, as well as the embodied emissions of the urban infrastructure ( [[#Butler--2014|Butler et al. 2014]] ; [[#Salat--2014|Salat et al. 2014]] ; [[#Ramaswami--2016|Ramaswami et al. 2016]] ; [[#Seto--2016|Seto et al. 2016]] ; [[#d’Amour--2017|d’Amour et al. 2017]] ). Thus, understanding trends in urban land use is essential for assessing energy behaviour in cities as well as long-term mitigation potential (Sections 8.4 and 8.6, and Figure 8.21).

This section draws on the literature to discuss three key trends in urban land expansion, and how those relate to GHG emissions.

First, urban land areas are growing rapidly all around the world. From 1975 to 2015, urban settlements expanded in size approximately 2.5 times, accounting for 7.6% of the global land area ( [[#Pesaresi--2016|Pesaresi et al. 2016]] ). Nearly 70% of the total urban expansion between 1992 and 2015 occurred in Asia and North America ( [[#Liu--2020a|Liu et al. 2020a]] ). By 2015, the extent of urban and built-up lands was between 0.5% and 0.6% of the total 130 Mkm 2 global ice-free land use, taking up other uses such as fertile cropland and natural ecosystems.

Second, as Figure 8.5 shows, urban population densities are declining, with significant implications for GHG emissions. From 1970 to 2010, while the global urban settlement extent doubled in size ( [[#Pesaresi--2016|Pesaresi et al. 2016]] ), most regions (grouped by the AR6 WGIII 10-region aggregation) exhibited a trend of decreasing urban population densities, suggesting expansive urban growth patterns. Urban population densities have consistently declined in Australia, Japan and New Zealand, and Europe, North America, and Southern Asia regions, across all city sizes. North America consistently had the lowest urban population densities. Notably, the Middle East region appears to be the only region exhibiting an overall increasing trend across all city-size groups, while Latin America and Caribbean appears to be relatively stable for all city sizes. While the larger cities in Africa and South-East Asia and Pacific exhibit slightly stable urban population densities, the small and medium-sized cities in those regions trend toward lower urban population densities. In large urban centres of Eastern Asia and North America, rapid decreases in earlier decades seem to have tapered. Compared to larger cities, small-medium urban areas with populations of less than 2 million have more declines in urban population densities and higher rates of urban land expansion ( [[#Güneralp--2020|Güneralp et al. 2020]] ).

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[[File:9e90a99f7f62f2e883664b1a214617f3 IPCC_AR6_WGIII_Figure_8_5.png]]

'''Figure 8.5: Urban population density by decade (1970–2010) grouped by the AR6 WGIII 10-region aggregation''' '''.''' Panel '''(a)''' displays the results from all case study locations with a population &gt;300,000. Panels (b) and (c) show results grouped by city size: '''(b)''' cities with a population &gt;2 million (large urban centres), and '''(c)''' those with a population &gt;300,000 but &lt;2 million (small and medium urban centres). Box plots show the median, first and third quartiles, and lower and upper mild outlier thresholds of bootstrapped average urban population densities at the turn of each decade. The estimates are shown on a logarithmic scale. The data shows an overall trend of declining urban population densities among all but one region in the last four decades, at varying rates – although the Latin America and Caribbean region indicates relatively constant urban population density over time. The Middle East region is the only region to present with an increase in urban population density across all city sizes. Source: adapted from [[#Güneralp--2020|Güneralp et al. (2020)]] .

This decline in urban densities is paralleled by an increase in ‘sprawl’, or ‘outward’ urban development. Urban expansion occurs in either one of three dimensions: (i) outward in a horizontal manner; (ii) upward, by way of vertical growth; or (iii) infill development, where unused, abandoned, or underutilised lands within existing urban areas are developed or rehabilitated (Figure 8.20). Outward expansion results in more urban land area and occurs at the expense of other land uses (i.e., the conversion and loss of cropland or forests). Vertical expansion results in more multi-storey buildings and taller buildings, more floor space per area, and an increase in urban built-up density. Every city has some combination of outward and upward growth in varying degrees ( [[#Mahtta--2019|Mahtta et al. 2019]] ) (Figure 8.6). That each city is comprised of different and multiple urban growth typologies suggests the need for differentiated mitigation strategies for different parts of a single city ( [[#8.6|Section 8.6]] and Figure 8.21). Recent research shows that the relative combination of outward versus upward growth is a reflection of its economic and urban development ( [[#Lall--2021|Lall et al. 2021]] ). That is, how a city grows – whether upward or outward – is a function of its economic development level. Upward growth, or more tall buildings, is a reflection of higher land prices ( [[#Ahlfeldt--2018|Ahlfeldt and McMillen 2018]] ; [[#Ahlfeldt--2020|Ahlfeldt and Barr 2020]] ).

An analysis of 478 cities with populations of more than 1 million people found that the predominant urban growth pattern worldwide is outward expansion, suggesting that cities are becoming more expansive than dense ( [[#Mahtta--2019|Mahtta et al. 2019]] ) (Figure 8.6). The study also found that cities within a geographic region exhibit remarkably similar patterns of urban growth. Some studies have found a mix of urban forms emerging around the world; an analysis of 194 cities identified an overall trend (from 1990 to 2015) toward urban forms that are a mixture of fragmented and compact ( [[#Lemoine-Rodriguez--2020|Lemoine-Rodriguez et al. 2020]] ). The exception to this trend is a group of large cities in Australia, New Zealand, and the United States that are still predominantly fragmented. The same study also identified small to medium-sized cities as the most dynamic in terms of their expansion and change in their forms.

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[[File:5dbe838d5cd2eb217723d31740ef6625 IPCC_AR6_WGIII_Figure_8_6a.png]] [[File:91407387862139eb9b603f404e48278d IPCC_AR6_WGIII_Figure_8_6b.png]]

'''Figure 8.6: (a) Distribution of growth typologies across 10 cities, and (b) sample of 64 cities by region with different patterns of urban growth.''' The empirical data is based on the Global Human Settlement Layer and backscatter power ratio for different patterns of urban growth across the sample of cities. In (b), the blue arrows indicate outward urban growth. Other urban patterns indicate stabilised (orange), mature upward (light blue), budding outward (green), and upward and outward (red). Note that with few exceptions, each city is comprised of multiple typologies of urban growth. Source: [[#Mahtta--2019|Mahtta et al. (2019)]] .

A third trend in is urban land growth taking place on agricultural land, carbon stocks, and other land uses (see ‘carbon stock’ and ‘AFOLU’ – agriculture, forestry, and other land uses – in Glossary). As Figure 8.7 shows, over 60% of the reported urban expansion (nearly 40,000 km 2 ) from 1970 to 2010 was formerly agricultural land ( [[#Güneralp--2020|Güneralp et al. 2020]] ). This percentage increased to about 70% for global urban expansion that occurred between 1992 and 2015, followed by grasslands (about 12%) and forests (about 9%) ( [[#Liu--2020a|Liu et al. 2020a]] ). In terms of percent of total urban land expansion, the largest conversion of agricultural lands to urban land uses from 1970 to 2010 took place in the Eastern Asia, and South-East Asia and Pacific regions; the largest proportional losses of natural land cover were reported for the North America and Australia, Japan and New Zealand regions ( [[#Güneralp--2020|Güneralp et al. 2020]] ). At a sub-regional level, agricultural land constituted the largest proportion of land converted to urban areas in China, India, Europe, Southeast Asian countries and the central United States between 1995 and 2015; in the eastern United States, most new urban land was converted from forests ( [[#Liu--2020a|Liu et al. 2020a]] ). Urban expansion through 2040 may lead to the loss of almost 65 Mt of crop production – a scenario that underscores the ongoing relationship between urbanisation and AFOLU ( [[#van%20Vliet--2017|van Vliet et al. 2017]] ) (Chapter 7).

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[[File:f011764c00693e5b4e80e7d07e72b3c0 IPCC_AR6_WGIII_Figure_8_7.png]]

'''Figure 8.7: Percent of total urban land expansion from other land covers, sorted by the AR6 WGIII 10-region aggregation (1970–2010).''' As urban land has expanded outward, other forms of land cover, including agriculture, ‘nature’ (e.g., forest, grassland, shrubland, water, and bare soil, all of which are disaggregated to the bottom half of the plot), and other land covers, have been displaced. Globally, agriculture comprises the majority (about 60%) of the land displaced by urban expansion since 1970. Forests and shrubland vegetation – important carbon stocks – also make up a significant proportion of displacement. The loss of carbon-sequestering land like forests and shrubland independently impacts climate change by reducing global carbon stocks. Eurasia is omitted because there are no case studies from that region that report land conversion data. Source: adapted from [[#Güneralp--2020|Güneralp et al. (2020)]] .

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=== 8.3.2 Informal Urban Settlements ===

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About 880 million people currently live in informal settlements – defined as unplanned areas operating outside of legal and regulatory systems, where residents have no legal claim over their property and have inadequate basic services and infrastructure ( [[#United%20Nations--2018|United Nations 2018]] ). Furthermore, upgrading informal settlements and inadequate housing is essential for improving resilience to climate change and well-being. Given the ubiquity of informal settlements in developing countries and LDCs, there is potential to harness informality to accelerate transitions to low-carbon urban development. There are several key reasons for their potential to mitigate GHG emissions. First, informal urban areas may not require large investments in retrofitting as they have developed with minimal investment in large-scale infrastructure. Second, these areas exhibit flexibility of development and can potentially be transformed into an urban form that supports low- or carbon-neutral infrastructure for transportation, energy use in residential buildings, and other sectors ( [[#Baurzhan--2016|Baurzhan and Jenkins 2016]] ; [[#Henneman--2016|Henneman et al. 2016]] ; [[#Byrne--2017|Byrne et al. 2017]] ; [[#Oyewo--2019|Oyewo et al. 2019]] ).

Informal urban areas can avoid the conventional trajectory of urban development by utilising large-scale strategies, such as micro-scale technologies, modal shifts towards compact, walkable urban form, as well as decentralised or meso-scale utilities of water, sanitation, and service centres – thereby mitigating emissions associated with transport and treating wastes ( [[#Tongwane--2015|Tongwane et al. 2015]] ; [[#Yang--2018|Yang et al. 2018]] ). Some specific mitigation options include spatial adjustments for walkability of neighbourhoods, low-energy-intensive mobility, low-energy-intensive residential areas, low-carbon energy sources at city scale, off-grid utilities, and electrification and enhancement of the urban ecology – all of which have multiple potential benefits ( [[#Colenbrander--2017|Colenbrander et al. 2017]] ; [[#Fang--2017|Fang et al. 2017]] ; [[#Laramee--2018|Laramee et al. 2018]] ; [[#van%20der%20Zwaan--2018|van der Zwaan et al. 2018]] ; [[#Wu--2018|Wu et al. 2018]] ; [[#Silveti--2019|Silveti and Andersson 2019]] ). Some of the co-benefits of the various mitigation options include more job opportunities and business start-ups, increased incomes, air quality improvement, and enhanced health and well-being ( [[#Gebreegziabher--2014|Gebreegziabher et al. 2014]] ; [[#Dagnachew--2018|Dagnachew et al. 2018]] ; [[#Keramidas--2018|Keramidas et al. 2018]] ; [[#Adams--2019|Adams et al. 2019]] ; [[#Ambole--2019|Ambole et al. 2019]] ; [[#Boltz--2019|Boltz et al. 2019]] ; [[#Moncada--2019|Moncada et al. 2019]] ; [[#Weimann--2019|Weimann and Oni 2019]] ; [[#Manga--2020|Manga et al. 2020]] ) ( [[#8.2|Section 8.2]] ).

Non-networked and non-centralised urban services and infrastructure in informal settlements, including sanitation, waste, water, and electricity, serve over 60% of the urban population in developing country cities ( [[#Lawhon--2018|Lawhon et al. 2018]] ). The alternatives of disruptive, hybrid, largely non-networked multiplicity of technologies applicable at micro to meso scales have potential for low-emissions development in urban areas of developing countries ( [[#Narayana--2009|Narayana 2009]] ; [[#Dávila--2012|Dávila and Daste 2012]] ; [[#Radomes%20Jr--2015|Radomes Jr and Arango 2015]] ; [[#Potdar--2016|Potdar et al. 2016]] ; [[#Grové--2018|Grové et al. 2018]] ). These technologies can be applied in the short term as responses with long-term influence on emissions reduction. The cumulative impact of the disruptive technologies can reduce emissions by 15 '''–''' 25% through enhanced emissions sinks in small and medium-sized cities ( [[#Tongwane--2015|Tongwane et al. 2015]] ; [[#du%20Toit--2018|du Toit et al. 2018]] ; [[#Nero--2018|Nero et al. 2018]] , 2019; [[#Frantzeskaki--2019|Frantzeskaki et al. 2019]] ; [[#Mantey--2019|Mantey and Sakyi 2019]] ; Singh and G. 2019).

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=== 8.3.3 Trends in Urban Greenhouse Gas Emissions ===

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One major innovation presented in AR6 – particularly in this chapter – is the inclusion of trend data on urban GHG emissions. Using multiple datasets in conjunction with the SSP and RCP scenarios, this chapter provides an estimate of urban GHG emissions from 1990 through 2100, based on a consumption-based approach. This innovation provides, for the first time, a temporal dimension to urban footprints considering different climate scenarios with implications for urban mitigation. The new analysis presents a comparison of ways urban emissions can evolve given different scenario contexts ( [[#8.3.4.2|Section 8.3.4.2]] ). Additionally, new research has quantified trends in urban CO 2 emissions and their key drivers across 91 global cities from 2000 to 2018 ( [[#Luqman--2021|Luqman et al. 2021]] ).

Figures 8.8 and 8.9 present key urban emission metrics and trends for six regions (based on the AR6 WGIII regional breakdown) – the first for the year 2015, and the latter for both 2000 and 2015.

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[[File:1374f4832addd1c1b4d359522502fad2 IPCC_AR6_WGIII_Figure_8_8.png]]

'''Figure 8.8: 2015 average urban greenhouse gas emissions per capita, considering carbon dioxide (CO''' 2 ''') and methane (CH''' 4 ''') emissions from a consumption-based perspective, alongside urban population, for regions represented in the AR6 WGIII 6-region aggregation.''' The average urban per capita emissions are given by the height of the bars while the width represents the urban population for a given region, based on 2015 values for both axes. Provided within the bars are the percentage shares of the urban population by region as a share of the total urban population. Source: synthesised based on data from [[#UN%20DESA--2019|UN DESA (2019)]] and [[#Gurney--2022|Gurney et al. (2022)]] .

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[[File:47ec500df9aab4e5d177461ef2f3dfcc IPCC_AR6_WGIII_Figure_8_9.png]]

'''Figure 8.9''' : '''Changes in six metrics associated with urban and national-scale carbon dioxide (CO''' 2 ''') and methane (CH''' 4 ''') emissions represented in the AR6 WGIII 6-region aggregation, with (a) 2000 and (b) 2015.''' The trends in [[#Luqman--2021|Luqman et al. (2021)]] were combined with the work of [[#Moran--2018|Moran et al. (2018)]] to estimate the regional urban CO 2 -eq share of global urban emissions, the urban share of national CO 2 -eq emissions, and the urban per capita CO 2 -eq emissions by region. This estimate is derived from consumption-based accounting that includes both direct emissions from within urban areas and indirect emissions from outside urban areas related to the production of electricity, goods, and services consumed in cities. It incorporates all CO 2 and CH 4 emissions except aviation, shipping and biogenic sources (i.e., land-use change, forestry, and agriculture). The dashed grey line represents the global average urban per capita CO 2 -eq emissions. The regional urban population share, regional CO 2 -eq share in total emissions, and national per capita CO 2 -eq emissions by region are given for comparison. Source: adapted from [[#Gurney--2022|Gurney et al. (2022)]] . 5

The key trends are as follows. First, the urban share of global GHG emissions (including CO 2 and CH 4 ) is substantive and continues to increase (Figure 8.9). Total urban CO 2 -eq emissions based on consumption-based accounting were estimated to be 25 GtCO 2 -eq, or 62% of the global total in 2015, and increased to an estimated 29 GtCO 2 -eq in 2020, representing about 67–72% of global emissions. This estimate includes all CO 2 and CH 4 emissions except aviation, shipping, and biogenic sources (i.e., land-use change, forestry, and agriculture). About 100 of the highest-emitting urban areas account for approximately 18% of the global carbon footprint ( [[#Moran--2018|Moran et al. 2018]] ). Globally, the urban share of national CO 2 -eq emissions increased 6 percentage points, from 56% in 2000 to 62% in 2015.

Second, while urban CO 2 emissions were increasing in all urban areas, the dominant drivers were dependent upon development level. Emissions growth in urban areas other than in Developed Countries was driven by increases in area and per capita emissions. Across all cities, higher population densities are correlated with lower per capita GHG emissions ( [[#Luqman--2021|Luqman et al. 2021]] ).

Third, the urban share of regional GHG emissions increased between 2000 and 2015, with much inter-region variation in the magnitude of the increase ( ''high confidence'' ) (Figure 8.9) ''.'' Between 2000 and 2015, the urban emissions share across AR6 WGIII regions (6-region aggregation) increased from 28% to 38% in Africa, from 46% to 54% in Asia and Pacific, from 62% to 72% in Developed Countries, from 57% to 62% in Eastern Europe and West-Central Asia, from 55% to 66% in Latin America and Caribbean, and from 68% to 69% in the Middle East.

Between 2000 and 2015, urban population, urban CO 2 -eq emissions, and national CO 2 -eq emissions increased as a share of the global total in the Asia and Pacific region while the share declined for Developed Countries. The urban share of total regional CO 2 -eq emissions decreased in Developed Countries from 58.2% (2000) to 40.0% (2015). Urban per capita CO 2 -eq and national per capita CO 2 -eq also increased in all regions except for the urban per capita CO 2 -eq value in the Developed Countries region, which declined slightly.

Fourth, the global average per capita urban GHG emissions increased between 2000 and 2015, with cities in the Developed Countries region producing nearly seven times more per capita than the lowest emitting region ( ''medium confidence'' ) ''.'' From 2000 to 2015, the global urban GHG emissions per capita increased from 5.5 to 6.2 tCO 2 -eq per person (an increase of 11.8%), with increases across five of the six regions: Africa increased from 1.3 to 1.5 tCO 2 -eq per person (22.6%); Asia and Pacific increased from 3.0 to 5.1 tCO 2 -eq per person (71.7%); Eastern Europe and West-Central Asia increased from 6.9 to 9.8 tCO 2 -eq per person (40.9%); Latin America and Caribbean increased from 2.7 to 3.7 tCO 2 -eq per person (40.4%); and the Middle East increased from 7.4 to 9.6 tCO 2 -eq per person (30.1%). Albeit starting from the highest level, Developed Countries had a decline of 11.4 to 10.7 tCO 2 -eq per person (–6.5%).

In 2015, regional urban per capita consumption-based CO 2 -eq emissions were lower than regional consumption-based national per capita CO 2 -eq emissions in five of the six regions. These regions in order of the difference are: Developed Countries (lower by 1.0 tCO 2 -eq per capita); Latin America and Caribbean (lower by 0.8 tCO 2 -eq per capita); Eastern Europe and West-Central Asia (lower by 0.7 tCO 2 -eq per capita); Middle East (lower by 0.4 tCO 2 -eq per capita); and Africa (lower by 0.2 tCO 2 -eq per capita); while higher only in the Asia and Pacific region (higher by 0.9 tCO 2 -eq per capita). All regions show convergence of the urban and national per capita CO 2 -eq, as the urban share of national emissions increases and dominates the regional total.

'''[[#footnote-004|5]]'''

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=== 8.3.4 Scenarios of Future Urbanisation and Greenhouse Gas Emissions ===

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This section assesses scenarios of future urban land expansion and urban GHG emissions. These scenarios have implications for the urban climate change mitigation strategies discussed in Sections 8.4 and 8.6 – in particular, in the context of the potential mitigation and development pathways for urban areas under certain scenarios.

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==== 8.3.4.1 Urban Land Expansion and Greenhouse Gas Emissions ====

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The uncertainties across urban land expansion forecasts, and associated SSPs, highlight an opportunity to pursue compact, low or net-zero GHG emissions development that minimises land-use competition, avoids carbon lock-in, and preserves carbon-sequestering areas like forests and grasslands (Sections 8.4. and 8.6, and Figure 8.21). Among the forecasts available are six global-scale spatially explicit studies of urban land expansion that have been published since AR5; four of the six, which present forecasts for each of the five SSPs, are considered in Table 8.1 and Figure 8.10 ( [[#Huang--2019|Huang et al. 2019]] ; [[#Li--2019b|Li et al. 2019b]] ; [[#Chen--2020a|Chen et al. 2020a]] ; [[#Gao--2020|Gao and O’Neill 2020]] ). All four have forecasts to 2050 but only three to 2100. One of the two not included here ( [[#van%20Vliet--2017|van Vliet et al. 2017]] ) also forecasts land displacement due to urban land expansion.

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[[File:4b0c988b22a6e1c82dcd309c5cd7c690 IPCC_AR6_WGIII_Figure_8_10.png]]

'''Figure 8.10: Forecasts of urban land expansion in 2050 and 2100 according to each SSP and AR6 WGIII 6-region aggregation, by study, where A: [[#Gao--2020|Gao and O’Neill (2020)]] , B: Chen''' '''et al''' '''.''' '''(2020a), C: Li''' '''et al.''' '''(2019), D: Huang''' '''et al.''' '''(2019), E: mean across studies, and F: median across all studies.''' Three studies ( [[#Li--2019b|Li et al. 2019b]] ; [[#Chen--2020a|Chen et al. 2020a]] ; [[#Gao--2020|Gao and O’Neill 2020]] ) report forecasts of urban land expansion to both 2050 and 2100. One study ( [[#Huang--2019|Huang et al. 2019]] ) reports the forecast only to 2050. Global current urban extents and the respective initial years vary slightly among the four studies. Years for values of current urban extent range from 2010 to 2020. See Table 8.1 for the range of data across the four studies and across SSPs. Source: data compiled form [[#Huang--2019|Huang et al. (2019)]] , Li et al. (2019), Chen et al. (2020), and [[#Gao--2020|Gao and O’Neill (2020)]] .

'''Table 8.1: Forecasts of total urban land per AR6 WGIII regio''' '''n (6-region aggregation) in 2050 for each SSP, with the median and range of estimates from four studies:''' '''Huang''' '''et al.''' '''(2019), Li''' '''et al.''' '''(2019), Chen''' '''et al.''' '''(2020), and [[#Gao--2020|Gao and O’Neill (2020)]] .''' Median estimates for the 2015 urban extent are based on the mean/median of estimates in [[#Huang--2019|Huang et al. (2019)]] and Chen et al. (2020). Median and range of estimates for each SSP in 2050 are based on values derived from the four studies: [[#Huang--2019|Huang et al. (2019)]] , Li et al. (2019), Chen et al. (2020), and [[#Gao--2020|Gao and O’Neill (2020)]] . While each study and SSP forecast increases in urban land in each region, the range and magnitude vary. Source: data compiled from [[#Huang--2019|Huang et al. (2019)]] , Li et al. (2019), Chen et al. (2020), and [[#Gao--2020|Gao and O’Neill (2020)]] .

{| class="wikitable"
|-
!
! '''2015'''

'''median'''

(km 2 ; range)

! '''SSP1'''

'''median'''

(km 2 ; range)

! '''SSP2'''

'''median'''

(km 2 ; range)

! '''SSP3'''

'''median'''

(km 2 ; range)

! '''SSP4'''

'''median'''

(km 2 ; range)

! '''SSP5'''

'''median'''

(km 2 ; range)

|-
| rowspan="2"| '''Africa'''

| '''64,423'''

| '''97,718'''

| '''116,486'''

| '''96,571'''

| '''119,971'''

| '''138,604'''

|-
| (41,472–87,373)

| (67,488–303,457)

| (59,638–274,683)

| (56,071–235,922)

| (54,633–344,645)

| (79,612–309,532)

|-
| rowspan="2"| '''Asia and Pacific'''

| '''241,430'''

| '''293,647'''

| '''355,445'''

| '''296,431'''

| '''329,485'''

| '''419,781'''

|-
| (167,548–315,312)

| (244,575–732,303)

| (236,677–624,659)

| (224,520–483,335)

| (240,639–632,678)

| (250,670–787,257)

|-
| rowspan="2"| '''Developed Countries'''

| '''260,167'''

| '''459,624'''

| '''506,301'''

| '''414,661'''

| '''496,526'''

| '''616,847'''

|-
| (188,660–331,674)

| (407,483–648,023)

| (431,592–614,592)

| (362,063–479,584)

| (411,320–586,058)

| (510,468–761,275)

|-
| rowspan="2"| '''Eastern Europe and West-Central Asia'''

| '''35,970'''

| '''63,625'''

| '''65,251'''

| '''59,779'''

| '''64,434'''

| '''76,994'''

|-
| (27,121–44,819)

| (42,990–91,612)

| (52,397–91,108)

| (44,129–90,794)

| (50,806–86,546)

| (54,039–93,008)

|-
| rowspan="2"| '''Latin America and Caribbean'''

| '''62,613'''

| '''86,236'''

| '''88,793'''

| '''93,804'''

| '''85,369'''

| '''102,343'''

|-
| (60,511–64,716)

| (63,507–163,329)

| (86,411–162,526)

| (65,286–162,669)

| (82,148–144,940)

| (82,961–167,102)

|-
| rowspan="2"| '''Middle East'''

| '''21,192'''

| '''51,351'''

| '''51,221'''

| '''48,032'''

| '''49,331'''

| '''55,032'''

|-
| (19,017–23,366)

| (187,68–69,266)

| (25,486–69,716)

| (19,412–63,236)

| (25,415–71,720)

| (33,033–75,757)

|-
| rowspan="2"| '''World'''

| '''685,795'''

| '''1,023,220'''

| '''1,174,742'''

| '''980,719'''

| '''1,123,900'''

| '''1,412,390'''

|-
| (669,246–702,343)

| (919,185– 1,991,579)

| (927,820–1,819,174)

| (850,681–1,493,454)

| (922,539–1,851,438)

| (1,018,321–2,180,816)

|}

Four overarching findings can be gleaned from these studies.

First, urban land areas will expand significantly by 2050 – by as much as 211% (see SSP5 forecast in [[#Huang--2019|Huang et al. 2019]] ), but likely within a large potential range of about 43–106% over the 2015 extent by 2050 – to accommodate the growing urban population (Table 8.1). Globally, there are large uncertainties and variations among the studies – and between the SSPs – about the rates and extent of future urban expansion, owing to uncertainties about economic development and population growth (ranges of estimates are provided in Table 8.1). Overall, the largest urban extents are forecasted under SSP5 (fossil fuel-intensive development) for both 2050 and 2100, whereas the smallest forecasted urban extents are under SSP3 (‘regional rivalry’). Forecasted global urban extents could reach between 1 and 2.2 million km 2 (median of 1.4 million km 2 , a 106% increase) in 2050 under SSP5, and between 0.85 and 1.5 million km 2 (median of 1 million km 2 , a 43% increase) in 2050 under SSP3. Under SSP1, which is characterised by a focus on sustainability with more compact, low-emissions development, urban extents could reach 1 million km 2 (range of 0.9 to 2 million km 2 , a 49% increase) in 2050. By 2100, the forecasted urban extents reach between 1.4 and 3.6 million km 2 (median 2.5 million km 2 ) under SSP5 and between 1 and 1.5 million km 2 (median 1.3 million km 2 ) under SSP3. Across the studies, substantially larger amounts of urban land expansion are expected after 2050 under SSP5 compared to other SSPs.

Second, there is a wide variation in estimates of urban land expansion across regions (using the AR6 WGIII 6-region aggregation). Across all four sets of forecasts, current urban land (circa 2015) is the largest in Developed Countries and in the Asia and Pacific region, with approximately two-thirds of the current urban extent occurring in those two regions (Table 8.1 and Figure 8.10). The largest increases in urban land by 2050 are expected in the Asia and Pacific and Developed Countries regions, across all the SSPs. However, the rate of increase in urban land in Eastern Europe and West-Central Asia, Latin America and Caribbean, and the Middle East is significant and urban land could more than double by 2050. One-third of the studies conclude that the United States, China, and India will experience continued urban land expansion at least until 2050 ( [[#Huang--2019|Huang et al. 2019]] ; [[#Li--2019b|Li et al. 2019b]] ). However, Li et al. (2019) report that, after 2050, China could experience a decrease in the rate of urban land expansion, while growth will continue for India. This is not surprising since India’s urban demographic transition will only get underway after the middle of the century, when the urban population is expected to exceed the rural population. In contrast, China’s urban demographic transition could be nearly complete by 2050.

Third, in spite of these general trends, there are differences in forecasted urban expansion in each region across the SSPs and studies, with [[#Huang--2019|Huang et al. (2019)]] forecasting the most future urban land expansion between 2015 and 2050. The range across studies is significant. Under SSP1, urban land areas could increase by between 69,000 and 459,000 km 2 in Developed Countries, 77,000–417,000 km 2 in Asia and Pacific, and 28,000–216,000 km 2 in Africa. Under SSP3, where urban land expansion is forecasted to be the lowest, urban land areas could increase by between 23,000 and 291,000 km 2 in Developed Countries, 57,000–168,000 km 2 in Asia and Pacific, and 16,000–149,000 km 2 in Africa. Under SSP5, where urban land expansion is forecasted to be the highest, urban land area could increase by between 129,000 and 573,000 km 2 in Developed Countries, 83,000–472,000 km 2 in Asia and Pacific, and 40,000–222,000 km 2 in Africa ( [[#Huang--2019|Huang et al. 2019]] ; [[#Li--2019b|Li et al. 2019b]] ; [[#Chen--2020a|Chen et al. 2020a]] ; [[#Gao--2020|Gao and O’Neill 2020]] ). By 2100, however, the Developed Countries region is expected to have the most urban expansion only in SSP5. In SSP2 and SSP4, the Developed Countries and Asia and Pacific regions have about equal amounts of new urban land; in SSP3, Asia and Pacific has more new urban land forecasted.

Fourth, both the range of estimates and their implications on land-use competition and urban life point to an opportunity for urban areas to consider their urban form when developing. Under the current urbanisation trajectory, 50–63% of newly expanded urban areas are expected to occur on current croplands ( [[#Chen--2020a|Chen et al. 2020a]] ). However, there is significant regional variation; between 2000 and 2040, 12.5% of cropland in China and 7.5% of cropland in the Middle East and North Africa could potentially be displaced due to urban expansion, compared to the world average of 3.7% ( [[#van%20Vliet--2017|van Vliet et al. 2017]] ). As urban clusters increase in size and greenspace is converted, future urban land expansion is expected to intensify UHIs and exacerbate night-time extreme temperatures. An urban footprint increase of 78–171% by 2050 over the urban footprint in 2015 is expected to result in average summer daytime and night-time warming in air temperature of 0.5°C–0.7°C, even up to about 3°C in certain locations ( [[#Huang--2019|Huang et al. 2019]] ). Furthermore, this urban expansion-induced warming is on average about half – and in certain locations nearly twice – as strong as warming that will be caused by GHG emissions based on the multi-model ensemble average forecasts in RCP4.5. In short, future urban expansion will amplify the background warming caused by GHG emissions, with extreme warming most pronounced during night-time ( ''very high confidence'' ) ( [[#Huang--2019|Huang et al. 2019]] ). These findings corroborate those in the Technical Summary of AR6 WGI ( [[#Arias--2021|Arias et al. 2021]] ).

The forecasted amounts and patterns of urban expansion presented here bear significant uncertainty due to underlying factors beyond mere methodological differences between the studies. These factors include potential changes in the social, economic, and institutional dynamics that drive urban land development across the world ( [[#Güneralp--2013|Güneralp and Seto 2013]] ). Some of these changes may come in the form of sudden shocks such as another global economic crisis or pandemic. The forecasts presented here do not take such factors into account.

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<span id="scenarios-of-future-urban-greenhouse-gas-emissions"></span>
==== 8.3.4.2 Scenarios of Future Urban Greenhouse Gas Emissions ====

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There remains little globally comprehensive literature on projections of future baseline GHG emissions from urban areas or scenarios deploying urban mitigation actions on the part of city or regional governments. This dearth of research rests on limited urban emissions data that are consistent and comparable across the globe, making review and synthesis challenging ( [[#Creutzig--2016b|Creutzig et al. 2016b]] ). Some research has presented urban emissions forecasts and related projections, including estimated urban energy use in 2050 ( [[#Creutzig--2015|Creutzig et al. 2015]] ), energy savings for low-carbon development ( [[#Creutzig--2016b|Creutzig et al. 2016b]] ), emission savings from existing and new infrastructure ( [[#Creutzig--2016a|Creutzig et al. 2016a]] ) (Figure 8.12), and urban emissions from buildings, transport, industry, and agriculture ( [[#IEA--2016a|IEA 2016a]] ).

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[[File:9f8728b289da1be7d363909cb7b8a52e IPCC_AR6_WGIII_Figure_8_11.png]]

'''Figure 8.11: Reference scenario and mitigation potential for global urban areas in the residential and commercial building, transport, waste, and material production sectors.''' The top red line indicates the reference scenario where no further emissions reduction efforts are taken, while the bottom dark line indicates the combined potential of reducing emissions across the sectors displayed. Wedges are provided for potential emissions savings associated with decarbonising residential buildings, commercial buildings, transport, waste, and materials as indicated in the legend. The shaded areas that take place among the wedges with lines indicate contributions from decarbonisation of electricity supply. Source: Re-used with permission from [[#Coalition%20for%20Urban%20Transitions--2019|Coalition for Urban Transitions (2019)]] .

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[[File:8f6524319a0618113e0ba6f8410999cf IPCC_AR6_WGIII_Figure_8_12.png]]

'''Figure 8.12: Urban infrastructure-based''' '''CO''' 2 '''-eq''' '''emission mitigation wedges.''' Urban infrastructure-based CO 2 -eq emission mitigation wedges across categories of existing (yellow/green), new (blue), and construction (grey) of urban infrastructure. The wedges include low-carbon energy systems and infrastructure, modal shift, tolls/tax, or behavioural change, and reductions from construction materials. Source: re-used with permission from [[#Creutzig--2016a|Creutzig et al. (2016a)]] .

In its study of about 700 urban areas with a population of at least 750,000, the [[#Coalition%20for%20Urban%20Transitions--2019|Coalition for Urban Transitions (2019)]] , attempts to quantify the urban portion of global GHG emissions, including the residential and commercial building, transport, waste, and material production (focusing on cement, aluminium, and steel) sectors, along with mitigation wedges aimed at staying below a 2°C level of atmospheric warming (Figure 8.11). Starting in 2015 with a global urban emissions total of almost 14 GtCO 2 -eq, the study projects an increase to 17.3 GtCO 2 -eq by 2050 – but this reduces to 1.8 GtCO 2 -eq by 2050 with the inclusion of mitigation wedges: 58% from buildings, 21% from transport, 15% materials efficiency, and 5% waste, with decarbonisation of electricity supply as a cross-cutting strategy across the wedges.

[[#footnote-003|6]] [[#footnote-002|7]] ''[[#footnote-001|8]]''

'''[[#footnote-000|9]]'''

Similar analysis by the urban networks C40 and GCoM examine current and future GHG emissions on smaller subsets of global cities, offering further insight on the potential emissions impacts of urban mitigation options. However, this analysis is limited to just a sample of the global urban landscape and primarily focused on cities in the Global North ( [[#GCoM--2018|GCoM 2018]] , 2019; C40 Cities et al. 2019) with methods to project avoided emissions in development ( [[#Kovac--2020|Kovac et al. 2020]] ). Different scopes of analysis between sectors, as well as limited knowledge of the impact of existing and new urban infrastructure, limit the possibility of direct comparisons in emissions. Still, the shares of urban mitigation potential ranges between 77.7% and 78.9% for combined strategies that involve decarbonised buildings and transport in urban infrastructure, and the wedges approach the remaining emissions reductions also considering construction materials and waste. This data supports urban areas pursuing a package of multiple, integrated mitigation strategies in planning for decarbonisation (Sections 8.4 and 8.6, and Figure 8.21).

The most comprehensive approach to-date for quantifying urban emissions within the global context ( [[#Gurney--2021|Gurney et al. 2021]] , 2022) combines the per capita carbon footprint estimates for 13,000 cities from [[#Moran--2018|Moran et al. (2018)]] with projections of the share of urban population ( [[#Jiang--2017|Jiang and O’Neill 2017]] ) within the IPCC’s SSP-RCP framework ( [[#van%20Vuuren--2014|van Vuuren et al. 2014]] , 2017a; [[#Riahi--2017|Riahi et al. 2017]] ). Urban emissions in seven SSP-RCP scenarios are shown in Figure 8.13 along with an estimate of the global total CO 2 -eq for context.

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[[File:223a72413dbe66a05cb21f380e49825d IPCC_AR6_WGIII_Figure_8_13.png]]

'''Figure 8.13: Carbon dioxide equivalent (''' '''CO''' 2 '''-eq''' ''') emissions from global urban areas in seven SSP-RCP variations spanning the 1990 to 2100 time period.''' Urban areas are aggregated to six regional domains based on the AR6 WGIII 6-region aggregation. Global total CO 2 -eq emissions (CO 2 and CH 4 (methane)) are also shown as marked by the dashed line. Future urban emissions in the context of SSP-RCP-Shared Policy Assumption (SPA) variations correspond to '''(a)''' SSP1-RCP1.9-SPA1, '''(b)''' SSP1-RCP2.6-SPA1, '''(c)''' SSP4-RCP3.4-SPA4, '''(d)''' SSP2-RCP4.5-SPA2, '''(e)''' SSP4-RCP6.0-SPA4, '''(f)''' SSP3-RCP7.0-SPA0 and '''(g)''' SSP5-RCP8.5 based on the marker scenario implementations. 6 The first three scenarios (a–c) with more stringent reduction pathways represent contexts where urban per capita emissions decline rapidly against various increases in urban population and are oriented to reach net-zero emissions within this century at different radiative forcing levels. SSP1 scenarios (a, b) represent contexts where urbanisation takes place rapidly while providing resource efficiency based on compact urban form ( [[#Jiang--2017|Jiang and O’Neill 2017]] ), with high levels of electrification ( [[#van%20Vuuren--2017b|van Vuuren et al. 2017b]] ; [[#Rogelj--2018|Rogelj et al. 2018]] ). The scenario context of SSP1-RCP1.9 represents a pathway in which there can be a transformative shift towards sustainability. Note that the scale of panels (f) and (g) is different from the other panels. 7 See Table 8.2 detailing the SSP-RCPs. Source: adapted from [[#Gurney--2022|Gurney et al. (2022)]] . 8

In 2020, total urban emissions (including CO 2 and CH 4 ) derived from consumption-based accounting were estimated to be 29 GtCO 2 -eq, representing between 67% and 72% of global CO 2 and CH 4 emissions, excluding aviation, shipping, and biogenic sources of emissions. By 2050, with moderate to low urban mitigation efforts, urban emissions are projected to rise to 34.0 GtCO 2 -eq (SSP2-RCP4.5) or 40.2 GtCO 2 -eq (SSP3-RCP7.0) – driven by growing urban population, infrastructure, and service demands. However, scenarios that involve rapid urbanisation can have different outcomes as seen in SSP1-RCP1.9 based on green growth, versus SSP5-RCP8.5 with the strongest carbon lock-in lacking any decarbonisation. Other scenarios involve mixed and/or low urbanisation, along with other differences, including the implementation of electrification, energy, and material efficiency, technology development and innovation, renewable energy preferences, and behavioural, lifestyle, and dietary responses (Table 8.2). With aggressive and immediate mitigation efforts to limit global warming to 1.5°C (&gt;50%) with no or limited overshoot, urban GHG emissions could approach net-zero and reach a maximum of 3.3 GtCO 2 -eq in 2050 (SSP1-RCP1.9). Under aggressive but not immediate urban mitigation efforts to limit global warming to 2°C (&gt;67%), urban emissions could reach 17.2 GtCO 2 -eq in 2050 (SSP1-RCP2.6).

'''Table 8.2: Synthesis of the urbanisation and scenario contexts of the urban emissions scenarios.''' Descriptions for urbanisation are adapted based on [[#Jiang--2017|Jiang and O’Neill (2017)]] while high, medium, low, or mixed levels in the scenario context are drawn from the marker model implementations of SSP1-SSP5 for IMAGE ( [[#van%20Vuuren--2017b|van Vuuren et al. 2017b]] ; [[#Rogelj--2018|Rogelj et al. 2018]] ), MESSAGE-GLOBIOM ( [[#Fricko--2017|Fricko et al. 2017]] ), AIM/CGE ( [[#Fujimori--2017|Fujimori et al. 2017]] ), GCAM ( [[#Calvin--2017|Calvin et al. 2017]] ), and REMIND-MAgPIE ( [[#Kriegler--2017|Kriegler et al. 2017]] ). The letters in parentheses refer to the panels in Figure 8.13. Energy and material efficiency relate to energy efficiency improvement and decrease in the intermediate input of materials, including steel and cement. Dietary responses include less meat-intensive diets. Implications for urban areas relate to the mitigation options in [[#8.4|Section 8.4]] . Source: adapted from [[#Gurney--2022|Gurney et al. (2022)]] .

{| class="wikitable"
|-
! rowspan="2"| '''SSP/RCP framework'''

! rowspan="2"| '''Urbanisation context'''

! colspan="6"| '''Scenario context'''

|-
! Electrification

! Energy and material efficiency

! Technology development/ innovation

! Renewable energy preferences

! Behavioural, lifestyle and dietary responses

! Afforestation and re-forestation

|-
| rowspan="2"| SSP1 RCP1.9 (a) RCP2.6 (b)

| rowspan="2"| Resource efficient, walkable and sustainable rapid urbanisation

| High

| High

| High

| High

| High

| High

|-
| colspan="6"| '''Implications for urban climate mitigation include:'''

– Electrification across the urban energy system while supporting flexibility in end-use

– Resource efficiency from a consumption-based perspective with cross-sector integration

– Knowledge and financial resources to promote urban experimentation and innovation

– Empowerment of urban inhabitants for reinforcing positive lock-in for decarbonisation

– Integration of sectors, strategies and innovations across different typologies and regions

|-
| '''SSP2''' RCP4.5 (d)

| Moderate progress

| Medium

| Medium

| Medium

| Medium

| Medium

| Medium

|-
| '''SSP3'''

RCP7.0 (f)

| Slow urbanisation, inadequate urban planning

| Medium

| Low

| Low

| Medium

| Low

| Low

|-
| '''SSP4''' RCP3.4 (c)

RCP6.0 (e)

| Pace of urbanisation differs with inequalities

| Mixed

| Mixed

| Mixed

| Mixed

| Mixed

| Mixed

|-
| '''SSP5''' RCP8.5 (g)

| Rapid urbanisation with carbon lock-in

| High

| Low

| High

| Low

| Low

| –

|}

When 2020 levels are compared to the values for the year 2030, urban areas that utilise multiple opportunities towards resource-efficient and walkable urbanisation are estimated to represent a savings potential of 9.8 GtCO 2 -eq of urban emissions, under SSP1-RCP1.9 scenario conditions, on the path towards net-zero CO 2 and CH 4 emissions. In contrast, urban emissions would increase by 3.4 GtCO 2 -eq from 2020 levels in 2030 under SSP2-RCP4.5 scenario conditions with moderate changes lacking ambitious mitigation action (Figure 8.14).

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[[File:e59438a1f7b544e1e9e6b2fc21b62681 IPCC_AR6_WGIII_Figure_8_14.png]]

'''Figure 8.14: Comparison of urban emissions under different urbanisation scenarios (Gt''' '''CO''' 2 '''-eq''' '''y''' '''r''' –1 ''') for the AR6 WGIII 6-region aggregation.''' The panels represent the estimated urban emissions change in two different scenarios for the time period 2020–2030. Panel '''(a)''' represents resource efficient and compact urbanisation while panel '''(b)''' represents urbanisation with moderate progress. The two scenarios are consistent with estimated urban emissions under the SSP1-RCP1.9-SPA1 and SSP2-RCP4.5-SPA2 scenarios, respectively (Figure 8.13). In both panels, urban emissions estimates for the year 2020 are marked by the lines for each region. In the resource efficient and compact scenario, various reductions in urban emissions that take place by 2030 are represented by the dashed areas within the bars. The remaining solid shaded areas represent the remaining urban emissions in 2030 for each region on the path towards net-zero emissions. The total reductions in urban emissions worldwide that are given by the last dashed grey bar in panel (a) is estimated to be 9.8 GtCO 2 -eq yr –1 between 2020 and 2030 in this scenario. In the scenario with moderate progress, there are no regions with reductions in urban emissions. Above the white lines that represent urban emissions in 2020, the grey shaded areas are the estimated increases for each region so that the total urban emissions would increase by 3.4 GtCO 2 -eq yr –1 from 2020 levels in 2030 under this scenario. The values are based on urban scenario analyses as given in Gurney et al. (2021, 2022) ''.'' Source: synthesised based on data from [[#Gurney--2022|Gurney et al. (2022)]] . 9

Among the 500 urban areas with the highest consumption-based urban emissions footprint in 2015 ( [[#Moran--2018|Moran et al. 2018]] ), urban-level emission scenarios under SSP1 conditions are constructed for 420 urban areas located across all regions of the world ( [[#Kılkış--2021a|Kılkış 2021a]] ). These scenarios are based on urban-level population projections by SSP ( [[#Kii--2021|Kii 2021]] ), trends in relevant CMIP6 scenarios ( [[#Gidden--2019|Gidden et al. 2019]] ), and a 100% renewable energy scenario ( [[#Bogdanov--2021|Bogdanov et al. 2021]] ). In the year 2020, the 420 urban areas are responsible for about 10.7 ± 0.32 GtCO 2 -eq, or 27% of the global total CO 2 and CH 4 emissions of about 40 GtCO 2 -eq, excluding aviation, shipping, and biogenic sources. Under three SSP1-based scenarios, the urban emissions of the 420 urban areas in 2030 is projected to be about 7.0 GtCO 2 -eq in SSP1-RCP1.9, 10.5 GtCO 2 -eq in SSP1-RCP2.6, and 5.2 GtCO 2 -eq in the SSP1 renewable energy scenario.

The Illustrative Mitigation Pathways (IMPs) represent different strategies for maintaining temperature goals that are compliant with the Paris Agreement, as well as their comparison with the continuation of current policies (Sections 1.5 and 3.2.5, and Table 8.3). The key characteristics that define the IMPs involve aspects of energy, land use, lifestyle, policy, and innovation. Urban areas provide cross-cutting contexts where each of these key characteristics can be enabled and have a particularly important role in the transformation pathways for renewable energy (IMP-Ren), low demand (IMP-LD), and shifting to sustainability (IMP-SP). Pathways that are compliant with the Paris Agreement include such urban implications as a reversal of decreasing land-use efficiency in urban areas to lower energy demand based on spatial planning for compact urban form ( [[#8.4.2|Section 8.4.2]] ), changes in urban infrastructure for supporting demand flexibility to handle variable energy supply ( [[#8.4.3|Section 8.4.3]] ), as well as policies and governance that are conducive to innovation in urban areas ( [[#8.5|Section 8.5]] ). Spatial planning for compact urban form can enable reduced energy demand and changes in service provisioning, including through walkable neighbourhoods and mixed land use, providing venues for socio-behavioural change towards active transport ( [[#8.4.5|Section 8.4.5]] ). Electrification and sector coupling in urban infrastructure can, for instance, be an important enabler of supporting higher penetrations of renewable energy in the energy system.

<div id="box-8.1:-does-urbanisation-drive-emissions?" class="h2-container box-container"></div>

<span id="box-8.1-does-urbanisation-d-rive-emissions"></span>
=== Box 8.1: Does Urbanisation Drive Emissions? ===

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Urbanisation can drive emissions if the process is accompanied by an income increase and higher levels of consumption ( [[#Sudmant--2018|Sudmant et al. 2018]] ). This is typically observed in countries with a large urban-rural disparity in income and basic services, and where urbanisation is accompanied by economic growth that is coupled to emissions. In addition, the outward expansion of urban land areas often results in the conversion and loss of agricultural land ( [[#Pandey--2018|Pandey et al. 2018]] ; [[#Liu--2019|Liu et al. 2019]] ), forests ( [[#Austin--2019|Austin et al. 2019]] ), and other vegetated areas, thereby reducing carbon uptake and storage ( [[#Quesada--2018|Quesada et al. 2018]] ) ( [[#8.3.1|Section 8.3.1]] ). Furthermore, the buildup and use of urban infrastructure (e.g., buildings, power, sanitation) requires large amounts of embodied energy and carbon (Figures 8.17 and 8.22). Building new and upgrading existing urban infrastructure could produce cumulative emissions of 226 GtCO 2 by 2050 ( [[#Bai--2018|Bai et al. 2018]] ).

However, for the same level of consumption and basic services, an average urban dweller often requires less energy than their rural counterparts, due to higher population densities that enable sharing of infrastructure and services, and economies of scale. Whether and to what extent such emission reduction potentials can be realised depends on how cities are designed and laid out (i.e., urban form – see [[#8.4.2|Section 8.4.2]] ) as well as how urban infrastructure is built and powered, such as the energy intensity of the city’s transportation system, type and level of urban services, the share of renewable energy, as well as the broader national and international economic and energy structure that supports the function of the cities (Sections 8.4.3 and 8.6).

Although population-dense cities can be more efficient than rural areas in terms of per capita energy use, and cities contribute less GHG emissions per person than low-density suburbs ( [[#Jones--2014|Jones and Kammen 2014]] ), there is some, albeit ''limited'' , evidence that larger cities are not more efficient than smaller ones ( [[#Fragkias--2013|Fragkias et al. 2013]] ; [[#Ribeiro--2019|Ribeiro et al. 2019]] ). A number of studies comparing urban and rural residents in the same country have shown that urban residents have higher per capita energy consumption and CO 2 emissions ( [[#Chen--2019a|Chen et al. 2019a]] ; [[#Hachaichi--2021|Hachaichi and Baouni 2021]] ). There is some evidence that the benefits of higher urban densities on reducing per capita urban GHG emissions may be offset by higher incomes, smaller household sizes, and, most importantly, higher consumption levels, thus creating a counter-effect that could increase GHG emissions with urbanisation ( [[#Gill--2018|Gill and Moeller 2018]] ).

Many studies have shown that the relationship between urbanisation and GHG emissions is dependent on the level and stage of urban development, and follows an inverted U-shaped relationship of the environmental Kuznets curve ( [[#Wang--2016|Wang et al. 2016]] , 2022; [[#Zhang--2017|Zhang et al. 2017]] ; [[#Xu--2018a|Xu et al. 2018a]] ; [[#Zhou--2019|Zhou et al. 2019]] ) (Sections 8.3.1 and 8.6, and Figure 8.20). Considering existing trends, earlier phases of urbanisation accompanied by rapid industrialisation, development of secondary industries, and high levels of economic growth, are correlated with higher levels of energy consumption and GHG emissions. However, more mature phases of urbanisation, with higher levels of economic development and establishment of the service sector, are correlated with lower levels of energy consumption and GHG emissions ( [[#Khan--2021|Khan and Su 2021]] ).

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<span id="urban-mitigation-options"></span>